International Journal of Software Engineering and Knowledge Engineering

Bogie is one of the most major mechanical part of railway train. Its security and reliability are of paramount importance. Since research in this field is still on the early stage, which focus on either mechanical structure without condition or binary coherent systems. A multistate network flow model has been proposed in this paper with consideration of components degradation level and functional interaction between them. Firstly, the structure and function of the bogie for CRH3 were made a detailed introduction. Then transmission paths of three types force on bogie were study to determine the network strcture. Different from other papers, arcs represent the components and nodes are the transitive relation. Arc capacity tends to be confirmed easily with utilization of performance deterioration of elements on bogie involved in force tranferring. Flow rate of each arc depends on both component' health status and the task it undertakes. Furthermore, the minimal paths (MPs) method and the recursive sum of disjoint products (RSDP) with ordering heuristics are used for system reliability calculation; and the relative probability importance of each basic component and system reliability with and without forehead information are given at last. The results show that the network flow model works well on CRH3 bogie, and can support as guidance of bogie system design, daily system operation and predictive maintenance.

This paper presents a risk-based maintenance decision making modeling methodology for railway asset maintenance optimization, which takes risk and maintenance cost objectives into consideration in the decision making process. A bottom-up risk analysis approach has been developed by using fuzzy reasoning approach (FRA) and fuzzy-analytical hierarchy process (Fuzzy-AHP) to produce a risk model. A total cost model has also been developed to estimate repair/renewal, maintenance and performance review costs. A risk-based maintenance decision making support model has then been developed by integrating the risk model with cost model in which multi-criteria decision making (MCDM) techniques are employed to process the proposed risk-based maintenance decision making support model. An illustrative example on a section of a track system maintenance decision selection is used to demonstrate the application of the proposed methodology. The results show that by using the proposed methodology the qualitative and quantitative risk data and information with maintenance costs associated with railway assets can be evaluated efficiently and effectively, which provide very useful information to railway engineers, managers, and decision makers.

Railway passenger transportation plays a fundamental role in China, reasonable revenue is the guarantee of railway's regularly development such as equipment replacement, technology enhancement, etc. Although many studies on the railway revenue models have been conducted, there is a lack of effective modeling which considers the multiple trains and the multiple levels of seats in real operation. Aiming to improve the revenue of railway transportation industry, this paper proposes a new optimization method for the train seat inventory control problem with the consideration of the multiple trains and the multiple levels of seats. As the in-depth research of this problem, an integer linear programming model is formulated which aims to maximize the total revenue of rail industry. The commercial software MATLAB with CPLEX solver is employed to obtain the approximate optimal solutions. The effectiveness and performance of the proposed approaches are testified by two examples implemented on a simple railway corridor and Wuhan–Guangzhou high-speed railway corridor. Moreover, sensitivity analysis experiments are given to explore the impact on the revenue if the model parameters are changed.

This paper proposes a method to optimize the conflicting tram signal priority requests at a multi-lines (M-lines) interchange intersection. The method is formulated on the basis of the spatiotemporal logic of complex switch area operations and the characteristics of multiple conflicting tram priority requests. Our study first depicts the interlocking logic of tram system operation according to the phases design and the spatial rules. Furthermore, it classifies the tram priority requests by the intersection entrances, priority phases and tram routes. Finally, it optimizes the serving sequences under the conditions of tram travel delay and switch operation delay. A microscopic traffic simulator, VISSIM, was used to simulate tram system and collect vehicle data. An external application program interface, VISSIM's COM-interface, was used to implement the proposed optimization method. The results show that the proposed method significantly outperforms the no-priority and the conventional first-come-first-served (FCFS) policy in terms of reducing tram delays. More specifically, the average tram delay is reduced by more than 26.6% over the no-priority policy and 13.0% on the FCFS policy in the initial operation. In addition, we find that the average delay which is caused by the switch operation delay increases with the tramline frequency. But the benefits of the proposed method are greater than those of the no-priority strategies.

For the real-time hardware-in-the-loop simulation of railway rolling stock electric drive system, the equation of equivalent circuit state is the most conventional modeling method. But this method will take up too many resources of real-time simulation system or is not easy to extend to other projects and difficult to adjust the variables on line. By taking the inductor, resistor, capacitor and converter in the equivalent circuit of four-quadrant converter system as the basic components, and using the Kirchhoff laws of voltage and current to combine the basic components, this paper established a new simulation model software based on MATLAB and realized real-time calculation based on dSPACE platform. In order to verify the feasibility and effectiveness of this model software, this author had finished a series of tests onboard and got enough experimental data to make a comparison with the results of the model software real-time calculation.

In software maintenance process, it is a fairly important activity to predict the severity of bug reports. However, manually identifying the severity of bug reports is a tedious and time-consuming task. So developing automatic judgment methods for predicting the severity of bug reports has become an urgent demand. In general, a bug report contains a lot of descriptive natural language texts, thus resulting in a high-dimensional feature set which poses serious challenges to traditionally automatic methods. Therefore, we attempt to use automatic feature selection methods to improve the performance of the severity prediction of bug reports. In this paper, we introduce a ranking-based strategy to improve existing feature selection algorithms and propose an ensemble feature selection algorithm by combining existing ones. In order to verify the performance of our method, we run experiments over the bug reports of Eclipse and Mozilla and conduct comparisons with eight commonly used feature selection methods. The experiment results show that the ranking-based strategy can effectively improve the performance of the severity prediction of bug reports by up to 54.76% on average in terms of $F$-measure, and it also can significantly reduce the dimension of the feature set. Meanwhile, the ensemble feature selection method can get better results than a single feature selection algorithm.